Xiangkai Zhu — accepted AI-conference papers

2026

Stage-Aware Graph Contrastive Learning with Node-oriented Mixture of Experts

Xiangkai Zhu, Yeyu Yan, Saiqin Long, Chao Li, Guanwen Chen, Longsheng Su

AAAI 2026technical

Text-attributed graphs (TAGs), which associate rich textual descriptions with each node, are widely employed to represent complex relationships among real-world textual entities. Currently, representation learning for TAGs leverages large language models (LLMs) to transform node-matched textual desc

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2025

Towards Pre-trained Graph Condensation via Optimal Transport

Yeyu Yan, Shuai Zheng, Wenjun Hui, Xiangkai Zhu, Dong Chen, Zhenfeng Zhu +2

NeurIPS 2025poster

Graph condensation (GC) aims to distill the original graph into a small-scale graph, mitigating redundancy and accelerating GNN training. However, conventional GC approaches heavily rely on rigid GNNs and task-specific supervision. Such a dependency severely restricts their reusability and generaliz…

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